Overview

Dataset statistics

Number of variables19
Number of observations1047571
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory159.8 MiB
Average record size in memory160.0 B

Variable types

Numeric17
Categorical1
Text1

Alerts

Heater Signal is highly imbalanced (60.2%)Imbalance
Velocity [km/h] has 126139 (12.0%) zerosZeros
Throttle [%] has 201260 (19.2%) zerosZeros
Motor Torque [Nm] has 135660 (12.9%) zerosZeros
Longitudinal Acceleration [m/s^2] has 11675 (1.1%) zerosZeros
Regenerative Braking Signal has 991325 (94.6%) zerosZeros
Heating Power CAN [kW] has 388157 (37.1%) zerosZeros
Requested Heating Power [W] has 377526 (36.0%) zerosZeros
AirCon Power [kW] has 726000 (69.3%) zerosZeros

Reproduction

Analysis started2023-08-15 19:28:12.793209
Analysis finished2023-08-16 20:48:18.192693
Duration1 day, 1 hour and 20 minutes
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Time [s]
Real number (ℝ)

Distinct38220
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean961.58536
Minimum0
Maximum3821.9
Zeros68
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:18.592291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.9
Q1381.2
median792.6
Q31339.5
95-th percentile2589.8
Maximum3821.9
Range3821.9
Interquartile range (IQR)958.3

Descriptive statistics

Standard deviation753.51539
Coefficient of variation (CV)0.78361779
Kurtosis0.89956661
Mean961.58536
Median Absolute Deviation (MAD)461.9
Skewness1.1112574
Sum1.0073289 × 109
Variance567785.44
MonotonicityNot monotonic
2023-08-16T22:48:19.202025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
236.1 69
 
< 0.1%
194.8 69
 
< 0.1%
195 69
 
< 0.1%
195.1 69
 
< 0.1%
195.2 69
 
< 0.1%
195.3 69
 
< 0.1%
195.4 69
 
< 0.1%
195.5 69
 
< 0.1%
195.6 69
 
< 0.1%
195.7 69
 
< 0.1%
Other values (38210) 1046881
99.9%
ValueCountFrequency (%)
0 68
< 0.1%
0.1 69
< 0.1%
0.2 69
< 0.1%
0.3 69
< 0.1%
0.4 69
< 0.1%
0.5 69
< 0.1%
0.6 69
< 0.1%
0.7 69
< 0.1%
0.8 69
< 0.1%
0.9 69
< 0.1%
ValueCountFrequency (%)
3821.9 1
< 0.1%
3821.8 1
< 0.1%
3821.7 1
< 0.1%
3821.6 1
< 0.1%
3821.5 1
< 0.1%
3821.4 1
< 0.1%
3821.3 1
< 0.1%
3821.2 1
< 0.1%
3821.1 1
< 0.1%
3821 1
< 0.1%

Velocity [km/h]
Real number (ℝ)

ZEROS 

Distinct128024
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.557424
Minimum0
Maximum152.26
Zeros126139
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:19.842002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.18
median41.55
Q368.92
95-th percentile114.49
Maximum152.26
Range152.26
Interquartile range (IQR)52.74

Descriptive statistics

Standard deviation35.361884
Coefficient of variation (CV)0.77620464
Kurtosis-0.45716596
Mean45.557424
Median Absolute Deviation (MAD)26.31
Skewness0.54808819
Sum47724636
Variance1250.4628
MonotonicityNot monotonic
2023-08-16T22:48:20.640742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126139
 
12.0%
0.08 1111
 
0.1%
0.09 929
 
0.1%
0.13 630
 
0.1%
0.11 629
 
0.1%
0.16 486
 
< 0.1%
0.5 473
 
< 0.1%
0.14 467
 
< 0.1%
1.16 412
 
< 0.1%
0.19 380
 
< 0.1%
Other values (128014) 915915
87.4%
ValueCountFrequency (%)
0 126139
12.0%
0.00049 1
 
< 0.1%
0.0005 1
 
< 0.1%
0.00058 1
 
< 0.1%
0.00059 1
 
< 0.1%
0.00063 1
 
< 0.1%
0.0008 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.00216 1
 
< 0.1%
ValueCountFrequency (%)
152.26 1
 
< 0.1%
152.25 2
 
< 0.1%
152.23 6
< 0.1%
152.22 1
 
< 0.1%
152.21 1
 
< 0.1%
152.19 2
 
< 0.1%
152.18 1
 
< 0.1%
152.17 5
< 0.1%
152.16 3
< 0.1%
152.15 2
 
< 0.1%

Elevation [m]
Real number (ℝ)

Distinct123886
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean530.79541
Minimum437
Maximum664.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:21.313616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437
5-th percentile475.45
Q1488.235
median530.71
Q3565.54
95-th percentile596.315
Maximum664.99
Range227.99
Interquartile range (IQR)77.305

Descriptive statistics

Standard deviation42.205499
Coefficient of variation (CV)0.079513685
Kurtosis-0.53959806
Mean530.79541
Median Absolute Deviation (MAD)38.02
Skewness0.33080697
Sum5.5604588 × 108
Variance1781.3042
MonotonicityNot monotonic
2023-08-16T22:48:21.931883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
513 8257
 
0.8%
572 8178
 
0.8%
574 7896
 
0.8%
576 7837
 
0.7%
575 7543
 
0.7%
475 7414
 
0.7%
484 6854
 
0.7%
479 6817
 
0.7%
654 6675
 
0.6%
573 6519
 
0.6%
Other values (123876) 973581
92.9%
ValueCountFrequency (%)
437 258
< 0.1%
437.01 1
 
< 0.1%
437.03 1
 
< 0.1%
437.06 1
 
< 0.1%
437.09 1
 
< 0.1%
437.12 1
 
< 0.1%
437.14 1
 
< 0.1%
437.17 1
 
< 0.1%
437.2 1
 
< 0.1%
437.22 1
 
< 0.1%
ValueCountFrequency (%)
664.99 2
< 0.1%
664.97 1
< 0.1%
664.96 1
< 0.1%
664.95 1
< 0.1%
664.94 1
< 0.1%
664.93 1
< 0.1%
664.92 1
< 0.1%
664.9 1
< 0.1%
664.89 1
< 0.1%
664.88 1
< 0.1%

Throttle [%]
Real number (ℝ)

ZEROS 

Distinct69337
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.55716
Minimum0
Maximum135.25
Zeros201260
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:22.487402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.4
median33.55
Q343.18
95-th percentile51.4
Maximum135.25
Range135.25
Interquartile range (IQR)30.78

Descriptive statistics

Standard deviation18.542905
Coefficient of variation (CV)0.64932594
Kurtosis-0.59975192
Mean28.55716
Median Absolute Deviation (MAD)12.2
Skewness-0.23339424
Sum29915652
Variance343.83931
MonotonicityNot monotonic
2023-08-16T22:48:23.757597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 201260
 
19.2%
42.1 3484
 
0.3%
42.85 3418
 
0.3%
42.4 3315
 
0.3%
43.5 3289
 
0.3%
45.5 3274
 
0.3%
44.75 3271
 
0.3%
42.25 3165
 
0.3%
41.65 3106
 
0.3%
45.95 3035
 
0.3%
Other values (69327) 816954
78.0%
ValueCountFrequency (%)
0 201260
19.2%
0.00017 1
 
< 0.1%
0.00032 1
 
< 0.1%
0.00043 1
 
< 0.1%
0.00103 1
 
< 0.1%
0.0026 1
 
< 0.1%
0.00266 1
 
< 0.1%
0.00342 1
 
< 0.1%
0.00473 1
 
< 0.1%
0.00663 1
 
< 0.1%
ValueCountFrequency (%)
135.25 1
< 0.1%
135.17 1
< 0.1%
135.1 1
< 0.1%
135.03 1
< 0.1%
134.96 1
< 0.1%
134.88 1
< 0.1%
134.81 1
< 0.1%
134.74 1
< 0.1%
134.67 1
< 0.1%
134.6 1
< 0.1%

Motor Torque [Nm]
Real number (ℝ)

ZEROS 

Distinct107803
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.955241
Minimum-87.9
Maximum249.5
Zeros135660
Zeros (%)12.9%
Negative246154
Negative (%)23.5%
Memory size16.0 MiB
2023-08-16T22:48:24.292741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-87.9
5-th percentile-46.91
Q10
median6.86
Q322.92
95-th percentile75.35
Maximum249.5
Range337.4
Interquartile range (IQR)22.92

Descriptive statistics

Standard deviation35.088659
Coefficient of variation (CV)3.2029108
Kurtosis3.7305683
Mean10.955241
Median Absolute Deviation (MAD)11.64
Skewness0.80019091
Sum11476393
Variance1231.214
MonotonicityNot monotonic
2023-08-16T22:48:24.835069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135660
 
12.9%
4.5 7131
 
0.7%
1.5 6906
 
0.7%
6 6404
 
0.6%
5.5 6256
 
0.6%
8.5 6168
 
0.6%
4 5834
 
0.6%
2 5453
 
0.5%
2.5 5306
 
0.5%
3.5 5294
 
0.5%
Other values (107793) 857159
81.8%
ValueCountFrequency (%)
-87.9 1
 
< 0.1%
-87.5 4
< 0.1%
-87.47 1
 
< 0.1%
-87.45 1
 
< 0.1%
-87.43 1
 
< 0.1%
-87.41 1
 
< 0.1%
-87.39 1
 
< 0.1%
-87.38 1
 
< 0.1%
-87.34 1
 
< 0.1%
-87.3 1
 
< 0.1%
ValueCountFrequency (%)
249.5 1
 
< 0.1%
249.25 1
 
< 0.1%
249 7
< 0.1%
248.84 1
 
< 0.1%
248.75 4
< 0.1%
248.72 1
 
< 0.1%
248.66 1
 
< 0.1%
248.5 7
< 0.1%
248.34 1
 
< 0.1%
248.28 1
 
< 0.1%

Longitudinal Acceleration [m/s^2]
Real number (ℝ)

ZEROS 

Distinct91557
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.3290765 × 10-5
Minimum-9.03
Maximum4.46
Zeros11675
Zeros (%)1.1%
Negative548069
Negative (%)52.3%
Memory size16.0 MiB
2023-08-16T22:48:25.398159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.03
5-th percentile-1.08
Q1-0.24
median-0.02
Q30.21
95-th percentile1.12108
Maximum4.46
Range13.49
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.63675256
Coefficient of variation (CV)-8688.0326
Kurtosis4.1999624
Mean-7.3290765 × 10-5
Median Absolute Deviation (MAD)0.23
Skewness0.37829565
Sum-76.77728
Variance0.40545382
MonotonicityNot monotonic
2023-08-16T22:48:26.035938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03 21184
 
2.0%
-0.04 16601
 
1.6%
-0.05 14166
 
1.4%
-0.02 13954
 
1.3%
-0.01 13725
 
1.3%
0.03 12947
 
1.2%
0.05 12909
 
1.2%
0.01 12523
 
1.2%
0.04 12510
 
1.2%
0.07 12362
 
1.2%
Other values (91547) 904690
86.4%
ValueCountFrequency (%)
-9.03 1
< 0.1%
-8.96 1
< 0.1%
-7.84 1
< 0.1%
-7.17 1
< 0.1%
-7.12 1
< 0.1%
-6.21 1
< 0.1%
-6.08 1
< 0.1%
-6.05 1
< 0.1%
-6 2
< 0.1%
-5.99 2
< 0.1%
ValueCountFrequency (%)
4.46 1
 
< 0.1%
4.38 1
 
< 0.1%
4.37 2
< 0.1%
4.34914 1
 
< 0.1%
4.34 3
< 0.1%
4.28482 1
 
< 0.1%
4.28 1
 
< 0.1%
4.27 1
 
< 0.1%
4.24 2
< 0.1%
4.23753 1
 
< 0.1%

Regenerative Braking Signal
Real number (ℝ)

ZEROS 

Distinct254
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.053316542
Minimum0
Maximum1
Zeros991325
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:26.595437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22437116
Coefficient of variation (CV)4.2082842
Kurtosis13.822992
Mean0.053316542
Median Absolute Deviation (MAD)0
Skewness3.9764867
Sum55852.863
Variance0.050342418
MonotonicityNot monotonic
2023-08-16T22:48:27.193379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 991325
94.6%
1 55458
 
5.3%
0.18 12
 
< 0.1%
0.6 12
 
< 0.1%
0.47 11
 
< 0.1%
0.57 11
 
< 0.1%
0.17 11
 
< 0.1%
0.74 11
 
< 0.1%
0.58 11
 
< 0.1%
0.78 10
 
< 0.1%
Other values (244) 699
 
0.1%
ValueCountFrequency (%)
0 991325
94.6%
0.001 1
 
< 0.1%
0.01 3
 
< 0.1%
0.01599 1
 
< 0.1%
0.01852 1
 
< 0.1%
0.02 2
 
< 0.1%
0.03 4
 
< 0.1%
0.03488 1
 
< 0.1%
0.03798 1
 
< 0.1%
0.04 4
 
< 0.1%
ValueCountFrequency (%)
1 55458
5.3%
0.9995 1
 
< 0.1%
0.9945 1
 
< 0.1%
0.99 6
 
< 0.1%
0.98498 1
 
< 0.1%
0.98 7
 
< 0.1%
0.97802 1
 
< 0.1%
0.97 4
 
< 0.1%
0.96753 1
 
< 0.1%
0.96298 1
 
< 0.1%

Battery Voltage [V]
Real number (ℝ)

Distinct104691
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean376.38779
Minimum301.8
Maximum394.75852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:27.871863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum301.8
5-th percentile353.74
Q1369.41
median379.5
Q3385.2
95-th percentile390.4
Maximum394.75852
Range92.95852
Interquartile range (IQR)15.79

Descriptive statistics

Standard deviation11.755479
Coefficient of variation (CV)0.031232359
Kurtosis1.5655255
Mean376.38779
Median Absolute Deviation (MAD)7.18453
Skewness-1.1590538
Sum3.9429294 × 108
Variance138.19128
MonotonicityNot monotonic
2023-08-16T22:48:28.508538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
387.2 3889
 
0.4%
386.9 3425
 
0.3%
387.1 3291
 
0.3%
390.9 2908
 
0.3%
381.4 2777
 
0.3%
388.8 2675
 
0.3%
381.2 2636
 
0.3%
388.2 2564
 
0.2%
387 2482
 
0.2%
381.3 2374
 
0.2%
Other values (104681) 1018550
97.2%
ValueCountFrequency (%)
301.8 2
< 0.1%
301.82 1
< 0.1%
301.87 2
< 0.1%
301.93 1
< 0.1%
301.97 1
< 0.1%
302.03 1
< 0.1%
302.07 1
< 0.1%
302.17 1
< 0.1%
302.22 1
< 0.1%
302.23 1
< 0.1%
ValueCountFrequency (%)
394.75852 1
< 0.1%
394.67 1
< 0.1%
394.66 1
< 0.1%
394.63686 1
< 0.1%
394.59 1
< 0.1%
394.57 1
< 0.1%
394.56 1
< 0.1%
394.55852 1
< 0.1%
394.55 1
< 0.1%
394.54 2
< 0.1%

Battery Current [A]
Real number (ℝ)

Distinct163557
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-19.39671
Minimum-404.38
Maximum144.49
Zeros113
Zeros (%)< 0.1%
Negative847109
Negative (%)80.9%
Memory size16.0 MiB
2023-08-16T22:48:29.012515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-404.38
5-th percentile-90.51
Q1-32.03
median-11.86
Q3-2.09
95-th percentile33.19
Maximum144.49
Range548.87
Interquartile range (IQR)29.94

Descriptive statistics

Standard deviation42.275088
Coefficient of variation (CV)-2.1794979
Kurtosis10.439287
Mean-19.39671
Median Absolute Deviation (MAD)12.6
Skewness-1.8216524
Sum-20319431
Variance1787.1831
MonotonicityNot monotonic
2023-08-16T22:48:29.620110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9 2439
 
0.2%
-0.6 2365
 
0.2%
-0.7 2091
 
0.2%
-1.4 1857
 
0.2%
-1 1855
 
0.2%
-2.2 1692
 
0.2%
-2.3 1553
 
0.1%
-0.8 1509
 
0.1%
-2.4 1409
 
0.1%
-1.9 1303
 
0.1%
Other values (163547) 1029498
98.3%
ValueCountFrequency (%)
-404.38 1
< 0.1%
-402.56 1
< 0.1%
-402.28 1
< 0.1%
-402.2 1
< 0.1%
-402.06 1
< 0.1%
-401.65 1
< 0.1%
-401.54 1
< 0.1%
-401.38 1
< 0.1%
-401.34698 1
< 0.1%
-400.6 1
< 0.1%
ValueCountFrequency (%)
144.49 1
< 0.1%
143.84 1
< 0.1%
143.53 1
< 0.1%
142.66 1
< 0.1%
142.04 2
< 0.1%
141.67 1
< 0.1%
141.34 1
< 0.1%
141.24 1
< 0.1%
140.87 1
< 0.1%
140.81 1
< 0.1%
Distinct1231
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.716244
Minimum0
Maximum32
Zeros3990
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:30.168112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median16
Q322
95-th percentile27
Maximum32
Range32
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3654955
Coefficient of variation (CV)0.46865493
Kurtosis-0.96191841
Mean15.716244
Median Absolute Deviation (MAD)6
Skewness0.057737004
Sum16463882
Variance54.250524
MonotonicityNot monotonic
2023-08-16T22:48:30.854448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 92455
 
8.8%
19 78108
 
7.5%
20 66326
 
6.3%
13 55025
 
5.3%
10 54470
 
5.2%
12 53407
 
5.1%
8 50332
 
4.8%
6 49571
 
4.7%
9 48702
 
4.6%
26 47818
 
4.6%
Other values (1221) 451357
43.1%
ValueCountFrequency (%)
0 3990
 
0.4%
0.02 1
 
< 0.1%
0.28 1
 
< 0.1%
0.54 1
 
< 0.1%
0.79 1
 
< 0.1%
1 4793
 
0.5%
1.05 1
 
< 0.1%
1.41 1
 
< 0.1%
1.77 1
 
< 0.1%
2 15532
1.5%
ValueCountFrequency (%)
32 3761
0.4%
31.94 1
 
< 0.1%
31.92 1
 
< 0.1%
31.83 1
 
< 0.1%
31.72 1
 
< 0.1%
31.6 1
 
< 0.1%
31.57 1
 
< 0.1%
31.49 1
 
< 0.1%
31.38 1
 
< 0.1%
31.26 1
 
< 0.1%

SoC [%]
Real number (ℝ)

Distinct9110
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.267215
Minimum0
Maximum88.5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:31.450147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.8
Q157.3
median68.5
Q376.6
95-th percentile84.7
Maximum88.5
Range88.5
Interquartile range (IQR)19.3

Descriptive statistics

Standard deviation15.166857
Coefficient of variation (CV)0.23238095
Kurtosis0.20510358
Mean65.267215
Median Absolute Deviation (MAD)9.1
Skewness-0.86191156
Sum68372041
Variance230.03356
MonotonicityNot monotonic
2023-08-16T22:48:31.986335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.9 5591
 
0.5%
71 5079
 
0.5%
72.1 4811
 
0.5%
71.3 4743
 
0.5%
71.1 4578
 
0.4%
65 4530
 
0.4%
70.9 4427
 
0.4%
72 4414
 
0.4%
70 4344
 
0.4%
64.2 4318
 
0.4%
Other values (9100) 1000736
95.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
15.4 533
0.1%
15.42 1
 
< 0.1%
15.43 2
 
< 0.1%
15.47 1
 
< 0.1%
15.48 2
 
< 0.1%
15.5 258
< 0.1%
15.53 1
 
< 0.1%
15.58 1
 
< 0.1%
15.6 86
 
< 0.1%
ValueCountFrequency (%)
88.5 283
< 0.1%
88.46 1
 
< 0.1%
88.41 1
 
< 0.1%
88.4 42
 
< 0.1%
88.36 1
 
< 0.1%
88.31 1
 
< 0.1%
88.3 64
 
< 0.1%
88.26 1
 
< 0.1%
88.21 1
 
< 0.1%
88.2 88
 
< 0.1%

Heating Power CAN [kW]
Real number (ℝ)

ZEROS 

Distinct11830
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2447261
Minimum0
Maximum40.04
Zeros388157
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:32.587445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.96
Q31.48
95-th percentile5.32
Maximum40.04
Range40.04
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.7900689
Coefficient of variation (CV)1.4381228
Kurtosis80.464564
Mean1.2447261
Median Absolute Deviation (MAD)0.96
Skewness5.6619189
Sum1303938.9
Variance3.2043467
MonotonicityNot monotonic
2023-08-16T22:48:33.192921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 388157
37.1%
5.32 32003
 
3.1%
1.52 25249
 
2.4%
1 22778
 
2.2%
1.04 21245
 
2.0%
1.08 21174
 
2.0%
1.12 19177
 
1.8%
0.96 19089
 
1.8%
0.8 19023
 
1.8%
1.48 18578
 
1.8%
Other values (11820) 461098
44.0%
ValueCountFrequency (%)
0 388157
37.1%
0.00362 1
 
< 0.1%
0.00396 1
 
< 0.1%
0.01 12
 
< 0.1%
0.01707 1
 
< 0.1%
0.02 8
 
< 0.1%
0.02856 1
 
< 0.1%
0.03 13
 
< 0.1%
0.03997 1
 
< 0.1%
0.04 73
 
< 0.1%
ValueCountFrequency (%)
40.04 1
< 0.1%
40.02 1
< 0.1%
40 1
< 0.1%
39.98 1
< 0.1%
39.96 1
< 0.1%
39.94 1
< 0.1%
39.92 1
< 0.1%
39.9 1
< 0.1%
39.88 1
< 0.1%
39.86 1
< 0.1%

Requested Heating Power [W]
Real number (ℝ)

ZEROS 

Distinct70490
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1189.3011
Minimum0
Maximum38527.75
Zeros377526
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:33.935237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median880
Q31440
95-th percentile5560
Maximum38527.75
Range38527.75
Interquartile range (IQR)1440

Descriptive statistics

Standard deviation1816.0593
Coefficient of variation (CV)1.5269971
Kurtosis66.881987
Mean1189.3011
Median Absolute Deviation (MAD)880
Skewness5.2944381
Sum1.2458773 × 109
Variance3298071.5
MonotonicityNot monotonic
2023-08-16T22:48:34.665611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 377526
36.0%
85 52279
 
5.0%
1000 27591
 
2.6%
880 27095
 
2.6%
1280 26724
 
2.6%
7000 22059
 
2.1%
1200 21438
 
2.0%
920 18135
 
1.7%
1080 16785
 
1.6%
1240 16543
 
1.6%
Other values (70480) 441396
42.1%
ValueCountFrequency (%)
0 377526
36.0%
0.11 1
 
< 0.1%
0.27 1
 
< 0.1%
0.43 1
 
< 0.1%
0.59 1
 
< 0.1%
0.656 1
 
< 0.1%
0.74 1
 
< 0.1%
0.75 1
 
< 0.1%
0.91 1
 
< 0.1%
1.06 1
 
< 0.1%
ValueCountFrequency (%)
38527.75 1
< 0.1%
38508.42 1
< 0.1%
38489.09 1
< 0.1%
38469.76 1
< 0.1%
38450.43 1
< 0.1%
38431.1 1
< 0.1%
38411.77 1
< 0.1%
38392.44 1
< 0.1%
38373.11 1
< 0.1%
38353.78 1
< 0.1%

AirCon Power [kW]
Real number (ℝ)

ZEROS 

Distinct10914
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17238651
Minimum-0.17
Maximum3.32
Zeros726000
Zeros (%)69.3%
Negative1461
Negative (%)0.1%
Memory size16.0 MiB
2023-08-16T22:48:35.303584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.17
5-th percentile0
Q10
median0
Q30.21
95-th percentile0.8
Maximum3.32
Range3.49
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.3740781
Coefficient of variation (CV)2.1699964
Kurtosis13.51904
Mean0.17238651
Median Absolute Deviation (MAD)0
Skewness3.3231224
Sum180587.1
Variance0.13993442
MonotonicityNot monotonic
2023-08-16T22:48:35.847373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 726000
69.3%
0.2 20110
 
1.9%
0.4 19368
 
1.8%
0.24 16780
 
1.6%
0.36 16449
 
1.6%
0.32 15924
 
1.5%
0.44 15340
 
1.5%
0.16 13993
 
1.3%
0.64 13877
 
1.3%
0.28 13200
 
1.3%
Other values (10904) 176530
 
16.9%
ValueCountFrequency (%)
-0.17 17
 
< 0.1%
-0.16 90
< 0.1%
-0.15 90
< 0.1%
-0.14 91
< 0.1%
-0.13 90
< 0.1%
-0.12 90
< 0.1%
-0.11 90
< 0.1%
-0.1 91
< 0.1%
-0.09 90
< 0.1%
-0.08 90
< 0.1%
ValueCountFrequency (%)
3.32 8
 
< 0.1%
3.28 10
 
< 0.1%
3.27 2
 
< 0.1%
3.26 3
 
< 0.1%
3.25 2
 
< 0.1%
3.24 66
< 0.1%
3.23 3
 
< 0.1%
3.22 1
 
< 0.1%
3.21 3
 
< 0.1%
3.2 121
< 0.1%

Heater Signal
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.0 MiB
1
964929 
0
 
82642

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1047571
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 964929
92.1%
0 82642
 
7.9%

Length

2023-08-16T22:48:36.401735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T22:48:36.779663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 964929
92.1%
0 82642
 
7.9%

Most occurring characters

ValueCountFrequency (%)
1 964929
92.1%
0 82642
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1047571
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 964929
92.1%
0 82642
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1047571
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 964929
92.1%
0 82642
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1047571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 964929
92.1%
0 82642
 
7.9%
Distinct8245
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.441513
Minimum-3.5
Maximum33.5
Zeros957
Zeros (%)0.1%
Negative14172
Negative (%)1.4%
Memory size16.0 MiB
2023-08-16T22:48:37.176727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.5
5-th percentile2.5
Q15
median10
Q322
95-th percentile30.5
Maximum33.5
Range37
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.6176977
Coefficient of variation (CV)0.71552197
Kurtosis-1.1996457
Mean13.441513
Median Absolute Deviation (MAD)7
Skewness0.39637118
Sum14080939
Variance92.500108
MonotonicityNot monotonic
2023-08-16T22:48:37.730530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 47639
 
4.5%
2.5 44484
 
4.2%
5.5 43742
 
4.2%
3.5 42741
 
4.1%
5 40912
 
3.9%
6 38826
 
3.7%
9.5 35033
 
3.3%
4 32429
 
3.1%
10 31246
 
3.0%
4.5 26369
 
2.5%
Other values (8235) 664150
63.4%
ValueCountFrequency (%)
-3.5 8620
0.8%
-3.49 9
 
< 0.1%
-3.48 3
 
< 0.1%
-3.47 2
 
< 0.1%
-3.46 7
 
< 0.1%
-3.45 4
 
< 0.1%
-3.44 9
 
< 0.1%
-3.43 3
 
< 0.1%
-3.42 2
 
< 0.1%
-3.41 7
 
< 0.1%
ValueCountFrequency (%)
33.5 1437
0.1%
33.49 1
 
< 0.1%
33.48 2
 
< 0.1%
33.47 1
 
< 0.1%
33.46 1
 
< 0.1%
33.45 4
 
< 0.1%
33.44 1
 
< 0.1%
33.43 2
 
< 0.1%
33.42 1
 
< 0.1%
33.41 1
 
< 0.1%
Distinct14537
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.033455
Minimum5
Maximum65.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:38.365262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q112
median35.5
Q341.5
95-th percentile53.5
Maximum65.05
Range60.05
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation16.091642
Coefficient of variation (CV)0.55424483
Kurtosis-1.360366
Mean29.033455
Median Absolute Deviation (MAD)12
Skewness-0.14419911
Sum30414606
Variance258.94096
MonotonicityNot monotonic
2023-08-16T22:48:38.971664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 96523
 
9.2%
40 35832
 
3.4%
40.5 27949
 
2.7%
41.5 26323
 
2.5%
42.5 25142
 
2.4%
41 23264
 
2.2%
37 23147
 
2.2%
39.5 22078
 
2.1%
43 20137
 
1.9%
43.5 18483
 
1.8%
Other values (14527) 728693
69.6%
ValueCountFrequency (%)
5 96523
9.2%
5.01 11
 
< 0.1%
5.02 5
 
< 0.1%
5.03 3
 
< 0.1%
5.04 12
 
< 0.1%
5.05 2
 
< 0.1%
5.06 11
 
< 0.1%
5.07 5
 
< 0.1%
5.08 3
 
< 0.1%
5.09 12
 
< 0.1%
ValueCountFrequency (%)
65.05 1
< 0.1%
65.03 1
< 0.1%
65 1
< 0.1%
64.97 1
< 0.1%
64.94 1
< 0.1%
64.92 1
< 0.1%
64.89 1
< 0.1%
64.86 1
< 0.1%
64.83 1
< 0.1%
64.81 1
< 0.1%
Distinct13809
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.038211
Minimum10
Maximum39.8413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:39.624879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13.34
Q122.84
median24.17
Q325.84
95-th percentile32.17
Maximum39.8413
Range29.8413
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.8814786
Coefficient of variation (CV)0.20307163
Kurtosis2.4531775
Mean24.038211
Median Absolute Deviation (MAD)1.5018
Skewness-0.38869919
Sum25181732
Variance23.828833
MonotonicityNot monotonic
2023-08-16T22:48:40.189698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.67 35093
 
3.3%
23.5 33527
 
3.2%
23.34 31890
 
3.0%
24 31869
 
3.0%
10 28916
 
2.8%
23.84 28761
 
2.7%
23.17 25566
 
2.4%
24.17 25325
 
2.4%
24.84 23311
 
2.2%
25.34 22123
 
2.1%
Other values (13799) 761190
72.7%
ValueCountFrequency (%)
10 28916
2.8%
10.01 14
 
< 0.1%
10.02 14
 
< 0.1%
10.03 11
 
< 0.1%
10.04 12
 
< 0.1%
10.05 9
 
< 0.1%
10.06 14
 
< 0.1%
10.07 14
 
< 0.1%
10.08 11
 
< 0.1%
10.09 12
 
< 0.1%
ValueCountFrequency (%)
39.8413 506
 
< 0.1%
39.84 8105
0.8%
39.83 2
 
< 0.1%
39.82846 1
 
< 0.1%
39.82 3
 
< 0.1%
39.81179 1
 
< 0.1%
39.81 1
 
< 0.1%
39.8 3
 
< 0.1%
39.79512 1
 
< 0.1%
39.79 3
 
< 0.1%

meas
Text

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.0 MiB
2023-08-16T22:48:40.690502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11523281
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTripA01.csv
2nd rowTripA01.csv
3rd rowTripA01.csv
4th rowTripA01.csv
5th rowTripA01.csv
ValueCountFrequency (%)
tripb14.csv 38220
 
3.6%
tripb09.csv 35561
 
3.4%
tripb01.csv 32518
 
3.1%
tripb12.csv 32255
 
3.1%
tripa06.csv 31645
 
3.0%
tripb04.csv 29550
 
2.8%
tripb08.csv 29139
 
2.8%
tripb36.csv 28521
 
2.7%
tripa08.csv 28059
 
2.7%
tripa15.csv 22348
 
2.1%
Other values (59) 739755
70.6%
2023-08-16T22:48:41.550235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1047571
9.1%
. 1047571
9.1%
r 1047571
9.1%
s 1047571
9.1%
c 1047571
9.1%
v 1047571
9.1%
p 1047571
9.1%
i 1047571
9.1%
B 579870
 
5.0%
A 467701
 
4.1%
Other values (10) 2095142
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6285426
54.5%
Uppercase Letter 2095142
 
18.2%
Decimal Number 2095142
 
18.2%
Other Punctuation 1047571
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 437622
20.9%
1 423035
20.2%
2 391686
18.7%
3 195656
9.3%
6 132209
 
6.3%
7 110821
 
5.3%
8 106575
 
5.1%
9 104766
 
5.0%
4 96871
 
4.6%
5 95901
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
r 1047571
16.7%
s 1047571
16.7%
c 1047571
16.7%
v 1047571
16.7%
p 1047571
16.7%
i 1047571
16.7%
Uppercase Letter
ValueCountFrequency (%)
T 1047571
50.0%
B 579870
27.7%
A 467701
22.3%
Other Punctuation
ValueCountFrequency (%)
. 1047571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8380568
72.7%
Common 3142713
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1047571
33.3%
0 437622
13.9%
1 423035
13.5%
2 391686
 
12.5%
3 195656
 
6.2%
6 132209
 
4.2%
7 110821
 
3.5%
8 106575
 
3.4%
9 104766
 
3.3%
4 96871
 
3.1%
Latin
ValueCountFrequency (%)
T 1047571
12.5%
r 1047571
12.5%
s 1047571
12.5%
c 1047571
12.5%
v 1047571
12.5%
p 1047571
12.5%
i 1047571
12.5%
B 579870
6.9%
A 467701
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11523281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1047571
9.1%
. 1047571
9.1%
r 1047571
9.1%
s 1047571
9.1%
c 1047571
9.1%
v 1047571
9.1%
p 1047571
9.1%
i 1047571
9.1%
B 579870
 
5.0%
A 467701
 
4.1%
Other values (10) 2095142
18.2%

Interactions

2023-08-16T22:47:54.925257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:25.149731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-16T22:46:49.205708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:02.093788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:20.339492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:34.008359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:48.882497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:48:07.626184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:30.702504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:38.673843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T01:16:46.114683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:25.103473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:40.702857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:52.200877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:03.480622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:12.540468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:21.897450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:32.036691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:40.513783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:49.712494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:03.283750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:21.416935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:34.627632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:49.997461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:48:08.748477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:31.154375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:39.418410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T01:16:47.258167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:27.121676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:41.506761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:52.851596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:04.033532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:13.054266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:22.465091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:32.622226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:41.087604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:50.222115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:04.440398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:22.511104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:35.281785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:51.070805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:48:09.816822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:31.594089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:39.835666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T01:16:47.885164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:28.694668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:42.232527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:53.547609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:04.649667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:13.616818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:23.004817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:33.079374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:41.543727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:50.966672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:05.642797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:23.553137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:35.858475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:52.004573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:48:10.941226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:32.091865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:40.280645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T01:16:48.482617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:30.198685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:42.945646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:54.348539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:05.171796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:14.144822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:23.554030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:33.624260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:42.020045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:51.781635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:06.870610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:24.639502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:36.536521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:53.004508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:48:11.681661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:32.520150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T21:28:40.697498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T01:16:49.033858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:31.029145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:43.725041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:45:54.999556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:05.719196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:14.647532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:24.103268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:34.084749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:42.571935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:46:52.573240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:07.966542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:25.668970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:37.128727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T22:47:53.931182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-08-16T22:48:12.645942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-16T22:48:14.802699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time [s]Velocity [km/h]Elevation [m]Throttle [%]Motor Torque [Nm]Longitudinal Acceleration [m/s^2]Regenerative Braking SignalBattery Voltage [V]Battery Current [A]Battery Temperature [°C]SoC [%]Heating Power CAN [kW]Requested Heating Power [W]AirCon Power [kW]Heater SignalAmbient Temperature [°C]Heat Exchanger Temperature [°C]Cabin Temperature Sensor [°C]meas
00.00.0574.00.00.00-0.030.0391.4-2.2021.086.90.085.00.4125.530.5024.5TripA01.csv
10.10.0574.00.00.000.000.0391.4-2.2121.086.90.085.00.4125.530.5024.5TripA01.csv
20.20.0574.00.00.00-0.010.0391.4-2.2621.086.90.085.00.4125.530.5024.5TripA01.csv
30.30.0574.00.00.00-0.030.0391.4-2.3021.086.90.085.00.4125.530.5024.5TripA01.csv
40.40.0574.00.00.00-0.030.0391.4-2.3021.086.90.085.00.4125.530.5024.5TripA01.csv
50.50.0574.00.00.00-0.010.0391.4-2.3021.086.90.085.00.4125.530.5024.5TripA01.csv
60.60.0574.00.00.00-0.010.0391.4-2.3021.086.90.085.00.4125.530.4524.5TripA01.csv
70.70.0574.00.00.00-0.030.0391.4-2.3121.086.90.085.00.4125.530.4024.5TripA01.csv
80.80.0574.00.00.38-0.010.0391.4-2.3621.086.90.085.00.4125.530.3524.5TripA01.csv
90.90.0574.00.00.12-0.010.0391.4-2.3721.086.90.085.00.4125.530.3024.5TripA01.csv
Time [s]Velocity [km/h]Elevation [m]Throttle [%]Motor Torque [Nm]Longitudinal Acceleration [m/s^2]Regenerative Braking SignalBattery Voltage [V]Battery Current [A]Battery Temperature [°C]SoC [%]Heating Power CAN [kW]Requested Heating Power [W]AirCon Power [kW]Heater SignalAmbient Temperature [°C]Heat Exchanger Temperature [°C]Cabin Temperature Sensor [°C]meas
141621416.20.0574.00.00.00.120.0380.59-6.6810.068.02.362080.00.01-3.546.024.17TripB37.csv
141631416.30.0574.00.00.00.110.0380.56-6.1410.068.02.122080.00.01-3.546.024.17TripB37.csv
141641416.40.0574.00.00.00.140.0380.51-5.2910.068.02.122080.00.01-3.546.024.17TripB37.csv
141651416.50.0574.00.00.00.110.0380.54-5.5310.068.02.122080.00.01-3.546.024.17TripB37.csv
141661416.60.0574.00.00.00.120.0380.59-6.0810.068.02.122080.00.01-3.546.024.17TripB37.csv
141671416.70.0574.00.00.00.120.0380.56-6.5110.068.02.122080.00.01-3.546.024.17TripB37.csv
141681416.80.0574.00.00.00.130.0380.51-6.9110.068.02.122080.00.01-3.546.024.17TripB37.csv
141691416.90.0574.00.00.00.120.0380.50-6.4610.068.02.122080.00.01-3.546.024.17TripB37.csv
141701417.00.0574.00.00.00.110.0380.50-5.7610.068.02.122080.00.01-3.546.024.17TripB37.csv
141711417.10.0574.00.00.00.110.0380.50-5.6010.068.02.122080.00.01-3.546.024.17TripB37.csv